The Application and Challenges of Deep Reinforcement Learning in Complex Environments

Deep reinforcement learning (DRL), as an important branch of machine learning, has shown strong potential for application in complex environmental decision-making problems in recent years. This article systematically reviews the current application status and development trends of DRL in fields such as gaming and virtual environments, robot control, resource management, and healthcare. Through a comprehensive analysis of existing literature, this paper has summarized the technical roadmap and solutions of DRL in addressing core challenges such as high-dimensional state spaces, sparse rewards, and partial observability, including hierarchical reinforcement learning frameworks, mixed reward designs, and memory based reinforcement learning methods. Meanwhile, this article delves into the opportunities and challenges faced by cutting-edge research directions such as multi-agent systems, security, and interpretability. Based on current research progress, possible paths for the future development of DRL have been proposed, including improving algorithm robustness, integrating interdisciplinary methods, and engineering considerations in practical deployment, and providing reference for the further development of deep reinforcement learning.